Dylan Rohan - a1844790

---

Week 1 - Assessment 1 - Part A

# Loading modules 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import scipy.stats as stats
from plotly.subplots import make_subplots
import plotly.graph_objects as go

# Selecting and preparing data
from sklearn import datasets
wine_df = pd.DataFrame(datasets.load_wine().data)
wine_df.columns = datasets.load_wine().feature_names
wine_class = datasets.load_wine().target
wine_df.insert(0, 'Class', wine_class)
wine_df.head()

	Class	alcohol	malic_acid	ash	alcalinity_of_ash	magnesium	total_phenols	flavanoids	nonflavanoid_phenols	proanthocyanins	color_intensity	hue	od280/od315_of_diluted_wines	proline
0	0	14.23	1.71	2.43	15.6	127.0	2.80	3.06	0.28	2.29	5.64	1.04	3.92	1065.0
1	0	13.20	1.78	2.14	11.2	100.0	2.65	2.76	0.26	1.28	4.38	1.05	3.40	1050.0
2	0	13.16	2.36	2.67	18.6	101.0	2.80	3.24	0.30	2.81	5.68	1.03	3.17	1185.0
3	0	14.37	1.95	2.50	16.8	113.0	3.85	3.49	0.24	2.18	7.80	0.86	3.45	1480.0
4	0	13.24	2.59	2.87	21.0	118.0	2.80	2.69	0.39	1.82	4.32	1.04	2.93	735.0

The dataset I have selected contains 13 attributes of 178 wines and appears free of missing data. It is usually used as a practice dataset for a classifier model, but I will be using it to compare the populations of the three classes of wine on a given attribute. The class indicate the cultivator of the wine, but all three come from the same region in Italy.

# Gathering information about the dataset
print("Missing Data:\n", wine_df.isna().sum())
print("Shape of dataframe: \n", wine_df.shape, "\n")
print("Attributes: \n", wine_df.columns, '\n')
print("Min values: \n", wine_df.min(), '\n')
print("Max values: \n", wine_df.max(), '\n')

Missing Data:
 Class                           0
alcohol                         0
malic_acid                      0
ash                             0
alcalinity_of_ash               0
magnesium                       0
total_phenols                   0
flavanoids                      0
nonflavanoid_phenols            0
proanthocyanins                 0
color_intensity                 0
hue                             0
od280/od315_of_diluted_wines    0
proline                         0
dtype: int64
Shape of dataframe: 
 (178, 14) 

Attributes: 
 Index(['Class', 'alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash',
       'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols',
       'proanthocyanins', 'color_intensity', 'hue',
       'od280/od315_of_diluted_wines', 'proline'],
      dtype='object') 

Min values: 
 Class                             0.00
alcohol                          11.03
malic_acid                        0.74
ash                               1.36
alcalinity_of_ash                10.60
magnesium                        70.00
total_phenols                     0.98
flavanoids                        0.34
nonflavanoid_phenols              0.13
proanthocyanins                   0.41
color_intensity                   1.28
hue                               0.48
od280/od315_of_diluted_wines      1.27
proline                         278.00
dtype: float64 

Max values: 
 Class                              2.00
alcohol                           14.83
malic_acid                         5.80
ash                                3.23
alcalinity_of_ash                 30.00
magnesium                        162.00
total_phenols                      3.88
flavanoids                         5.08
nonflavanoid_phenols               0.66
proanthocyanins                    3.58
color_intensity                   13.00
hue                                1.71
od280/od315_of_diluted_wines       4.00
proline                         1680.00
dtype: float64 

Section 1 - Violin plots

Below I explore the data using multiple violin plots and do a bit of statistical analyses to determine whether any of the attributes are significanlty different among the three classes using an anova. It was found that the three populations of wine were statistically significant below the 0.05 threshold in all attributes. This means that depending on which attribute you find most important in an alcoholic drink, one of these cultivators of wine would be better suited to your tastes than the others.

(The default hover seems to supply all the information one might want, but it can be altered with the hovertemplate attribute)

# Demonstarting how you can quickly make subplots to view all attributes
#### UNITS NOT PROVIDED IN DOCUMENTION ####

# Creatuing list of attributes of interest to loop through
attributes = ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 
              'flavanoids', 'nonflavanoid_phenols','proanthocyanins', 'color_intensity', 'hue',
              'od280/od315_of_diluted_wines', 'proline']

# Providing subtitles
subtitles = []
for attribute in attributes:
    subtitles.append(attribute + " in each class of wine")
Subtitles = []
for string in subtitles:
    Subtitles.append(string.title())
    
# Creating a plotly figure object
wine_fig = make_subplots(
    rows = len(attributes), 
    cols = 1,
    subplot_titles = Subtitles,
    vertical_spacing = 0.02)

# Looping figures to generate each subplot
i = 1
for attribute in attributes:
    wine_fig.add_trace(
        go.Violin(x = wine_df['Class'], y = wine_df[attribute], 
                  box_visible=True,
                  name = attribute), #If you want a legend
                  row = i, col=1, 
        )              
    i=i+1
    
# Cleaning the figure up
wine_fig.update_layout(height=5000, width=800, 
                       title_text="Violin Subplots For Each Attribute",
                       showlegend=False)
i = 1
for attribute in attributes:
        wine_fig.update_xaxes(title_text="Class", row = i, col=1)                           # Adding x-axis label
        wine_fig.update_yaxes(title_text= attribute.title() + " Content", row = i, col=1)   # Adding y-axis label
        i = i+1
        
wine_fig

# Demonstrating other forms the violin plot can have on plotly

# With scatter points
wine_fig2 = px.violin(wine_df, x = 'Class', y = 'alcohol', color = 'Class', points='all')
wine_fig2.update_layout(height=500, width=800, 
                       title_text="Violin plot with associated alcohol instances",
                       showlegend=False)

# Overlayed, you can click the legend to remove layers
wine_fig3 = px.violin(wine_df, x = 'alcohol', color = 'Class', violinmode='overlay')
wine_fig3.update_layout(height=500, width=800, 
                       title_text="Overlayed violin plots for the alcohol content of each Class of wine",
                       showlegend=True)
        

# One-way ANOVA
count = 1
for i in ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash','magnesium', 'total_phenols', 'flavanoids', 
          'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']:
    fstat, p_value = stats.f_oneway(wine_df[i][wine_df['Class'] == 0],
                                    wine_df[i][wine_df['Class'] == 1],
                                    wine_df[i][wine_df['Class'] == 2])
    f_crit = stats.f.ppf(q=1-.005, dfn=3-1, dfd=178-3)
    if (p_value <= 0.05)==True & (fstat > f_crit)==True:
        print(count,") When comparing the ", i , " in/for each class, the classes were statistically different:\n", "p-value =", p_value)
        print("F-critical = ",f_crit,", f-stat =",  fstat)
        print()
        count = count + 1

1 ) When comparing the  alcohol  in/for each class, the classes were statistically different:
 p-value = 3.319503795619655e-36
F-critical =  5.462017136271799 , f-stat = 135.07762424279912

2 ) When comparing the  malic_acid  in/for each class, the classes were statistically different:
 p-value = 4.127228798041834e-14
F-critical =  5.462017136271799 , f-stat = 36.94342496318366

3 ) When comparing the  ash  in/for each class, the classes were statistically different:
 p-value = 4.149967974793239e-06
F-critical =  5.462017136271799 , f-stat = 13.312901199991039

4 ) When comparing the  alcalinity_of_ash  in/for each class, the classes were statistically different:
 p-value = 9.444472938826817e-14
F-critical =  5.462017136271799 , f-stat = 35.77163740730924

5 ) When comparing the  magnesium  in/for each class, the classes were statistically different:
 p-value = 8.963395439251042e-06
F-critical =  5.462017136271799 , f-stat = 12.4295843381499

6 ) When comparing the  total_phenols  in/for each class, the classes were statistically different:
 p-value = 2.1376700154385954e-28
F-critical =  5.462017136271799 , f-stat = 93.73300962036718

7 ) When comparing the  flavanoids  in/for each class, the classes were statistically different:
 p-value = 3.5985858307136404e-50
F-critical =  5.462017136271799 , f-stat = 233.92587268154935

8 ) When comparing the  nonflavanoid_phenols  in/for each class, the classes were statistically different:
 p-value = 3.88804090047893e-11
F-critical =  5.462017136271799 , f-stat = 27.575417146965872

9 ) When comparing the  proanthocyanins  in/for each class, the classes were statistically different:
 p-value = 5.125358737546706e-12
F-critical =  5.462017136271799 , f-stat = 30.27138317022762

10 ) When comparing the  color_intensity  in/for each class, the classes were statistically different:
 p-value = 1.162008021927618e-33
F-critical =  5.462017136271799 , f-stat = 120.66401844100312

11 ) When comparing the  hue  in/for each class, the classes were statistically different:
 p-value = 5.9176622163620664e-30
F-critical =  5.462017136271799 , f-stat = 101.31679539030002

12 ) When comparing the  od280/od315_of_diluted_wines  in/for each class, the classes were statistically different:
 p-value = 1.393104956942896e-44
F-critical =  5.462017136271799 , f-stat = 189.97232057888917

13 ) When comparing the  proline  in/for each class, the classes were statistically different:
 p-value = 5.783168356105498e-47
F-critical =  5.462017136271799 , f-stat = 207.92037390217774

Section 2 - Seaborn horizontal boxplot

planets_df = sns.load_dataset('planets')
planets_df.head()
# This dataset seems to have a list of observations/ calculations for a planet when using a specific method.

	method	number	orbital_period	mass	distance	year
0	Radial Velocity	1	269.300	7.10	77.40	2006
1	Radial Velocity	1	874.774	2.21	56.95	2008
2	Radial Velocity	1	763.000	2.60	19.84	2011
3	Radial Velocity	1	326.030	19.40	110.62	2007
4	Radial Velocity	1	516.220	10.50	119.47	2009

order =["Radial Velocity", "Imaging", "Eclipse Timing Variations", "Transit", "Astrometry",
                        "Transit Timing Variations", "Orbital Brightness Modulation", "Microlensing", "Pulsar Timing",
                        "Pulsation Timing Variations"]

copied_fig = plt.figure(figsize = (8,8))
ax = sns.boxplot(data = planets_df, # selecting the data
                 x = 'distance',
                 y = 'method',
                 orient = 'h', # making horizontl boxplots
                 palette = 'vlag', saturation = 0.7, # getting the colour right
                 order = order, # setting the order to match that in the picture
                 whis =200) 
# Setting axis to match source image
ax.set_xscale('log')
ax.semilogx()
# swarmplot
sns.swarmplot(data = planets_df,
              x = 'distance',
              y = 'method',
              color = "black", 
              orient = 'h', 
              size = 2.2, 
              order = order, 
              ) # including the swarm plot as seen in picture


ax.xaxis.grid(True)
sns.despine(left =True)
ax.set_ylabel(None)
ax.set_xlabel("distance")
plt.axis('auto')

C:\Users\rohad\anaconda3\lib\site-packages\seaborn\categorical.py:1296: UserWarning:

10.7% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.

(0.8717250189453656, 13163.554734132505, 8.5, -0.5)